Method for reservoir evaluation employing non-equilibrium asphaltene component

ABSTRACT

The present disclosure relates to a method for characterizing a hydrocarbon reservoir of interest traversed by at least one wellbore that includes (a) using a numerical model to simulate over geological time a non-equilibrium concentration of an asphaltene component as a function of location within the wellbore, (b) analyzing fluid samples acquired from at least one wellbore that traverses the reservoir of interest to measure concentration of the asphaltene component as a function of location within the wellbore, (c) comparing the non-equilibrium concentration of the asphaltene component as a function of location within the wellbore resulting from the simulation of (a) to the concentration of the asphaltene component as a function of location within the wellbore as measured in (b), and characterizing the reservoir of interest based upon the comparing of (c).

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit of U.S. Provisional Patent ApplicationSer. No. 62/082,491, filed Nov. 20, 2014, which is herein incorporatedby reference.

BACKGROUND OF THE DISCLOSURE

Wellbores or boreholes may be drilled to, for example, locate andproduce hydrocarbons. During a drilling operation, it may be desirableto evaluate and/or measure properties of encountered formations andformation fluids. In some cases, a drillstring is removed and a wirelinetool deployed into the borehole to test, evaluate and/or sample theformations and/or formation fluid(s). In other cases, the drillstringmay be provided with devices to test and/or sample the surroundingformations and/or formation fluid(s) without having to remove thedrillstring from the borehole.

Formation evaluation may involve drawing fluid from the formation into adownhole tool for testing and/or sampling. Various devices, such asprobes and/or packers, may be extended from the downhole tool to isolatea region of the wellbore wall, and thereby establish fluid communicationwith the subterranean formation surrounding the wellbore. Fluid may thenbe drawn into the downhole tool using the probe and/or packer. Withinthe downhole tool, the fluid may be directed to one or more fluidanalyzers and sensors that may be employed to detect properties of thefluid while the downhole tool is stationary within the wellbore.

SUMMARY

The present disclosure relates to a method for characterizing ahydrocarbon reservoir of interest traversed by at least one wellborethat includes (a) using a numerical model to simulate over geologicaltime a non-equilibrium concentration of an asphaltene component as afunction of location within the wellbore, (b) analyzing fluid samplesacquired from at least one wellbore that traverses the reservoir ofinterest to measure concentration of the asphaltene component as afunction of location within the wellbore, (c) comparing thenon-equilibrium concentration of the asphaltene component as a functionof location within the wellbore resulting from the simulation of (a) tothe concentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b), and characterizing the reservoirof interest based upon the comparing of (c).

The present disclosure also relates to a system for characterizing ahydrocarbon reservoir of interest traversed by at least one wellborethat includes a downhole tool configured to collect formation fluid fromthe hydrocarbon reservoir of interest within a sample chamber disposedin a downhole tool and a controller including machine readableinstructions disposed on a memory device. The instructions monitor orcontrol operations of the downhole tool to (a) use a numerical model tosimulate over geological time a non-equilibrium concentration of anasphaltene component as a function of location within the wellbore, (b)analyze fluid samples acquired from at least one wellbore that traversesthe reservoir of interest to measure concentration of the asphaltenecomponent as a function of location within the wellbore, (c) compare thenon-equilibrium concentration of the asphaltene component as a functionof location within the wellbore resulting from the simulation of (a) tothe concentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b), and (d) characterize thereservoir of interest based upon the comparison of (c).

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1A is a schematic diagram of an embodiment of a petroleum reservoirfluid analysis tool that can be used as part of the methodology of thepresent disclosure;

FIG. 1B is a schematic diagram of an embodiment of a fluid analysismodule suitable for use in the tool of FIG. 1A; and

FIGS. 2A-2C, collectively, are a flow chart of an embodiment of dataanalysis operations that are part of the workflow for reservoir analysisin accordance with the present disclosure.

DETAILED DESCRIPTION

It is to be understood that the following disclosure provides manydifferent embodiments, or examples, for implementing different featuresof various embodiments. Specific examples of components and arrangementsare described below to simplify the present disclosure. These are, ofcourse, merely examples and are not intended to be limiting. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.Moreover, the formation of a first feature over or on a second featurein the description that follows may include embodiments in which thefirst and second features are formed in direct contact, and may alsoinclude embodiments in which additional features may be formedinterposing the first and second features, such that the first andsecond features may not be in direct contact.

The present disclosure relates to systems and methods for reservoircharacterization, such as simulating asphaltene disequilibrium inreservoirs. Petroleum includes a complex mixture of hydrocarbons ofvarious molecular weights, plus other organic compounds. The molecularcomposition of petroleum varies widely from formation to formation. Theproportion of hydrocarbons in the mixture is highly variable and rangesfrom as much as 97 percent by weight in the lighter oils to as little as50 percent in the heavier oils and bitumens. The hydrocarbons inpetroleum are mostly alkanes (linear or branched), cycloalkanes,aromatic hydrocarbons, or more complicated chemicals like asphaltenes.The other organic compounds in petroleum may contain carbon dioxide(CO₂), nitrogen, oxygen, and sulfur, and trace amounts of metals such asiron, nickel, copper, and vanadium.

Petroleum may be characterized by SARA fractionation where asphaltenesare removed by precipitation with a paraffinic solvent and thedeasphalted oil separated into saturates, aromatics, and resins bychromatographic separation.

Saturates include alkanes and cycloalkanes. The alkanes, also known asparaffins, are saturated hydrocarbons with straight or branched chainswhich contain only carbon and hydrogen and have the general formulaC_(n)H_(2n+2). They generally have from 5 to 40 carbon atoms permolecule, although trace amounts of shorter or longer molecules may bepresent in the mixture. The alkanes include methane (CH₄), ethane(C₂H₆), propane (C₃H₈), i-butane (iC₄H₁₀), n-butane (nC₄H₁₀), i-pentane(iC₅H₁₂), n-pentane (nC₅H₁₂), hexane (C₆H₁₄), heptane (C₇H₁₆), octane(C₈H₁₈), nonane (C₉H₂₀), decane (C₁₀H₂₂), hendecane (C₁₁H₂₄)—alsoreferred to as endecane or undecane, dodecane (C₁₂H₂₆), tridecane(C₁₃H₂₈), tetradecane (C₁₄H₃₀), pentadecane (C₁₅H₃₂) and hexadecane(C₁₆H₃₄). The cycloalkanes, also known as napthenes, are saturatedhydrocarbons which have one or more carbon rings to which hydrogen atomsare attached according to the formula C_(n)H_(2n). Cycloalkanes havesimilar properties to alkanes but have higher boiling points. Thecycloalkanes include cyclopropane (C₃H₆), cyclobutane (C₄H₈),cyclopentane (C₅H₁₀), cyclohexane (C₆H₁₂), and cycloheptane (C₇H₁₄).

The aromatic hydrocarbons are unsaturated hydrocarbons which have one ormore planar six-carbon rings called benzene rings, to which hydrogenatoms are attached with the formula C_(n)H_(n). They tend to burn with asooty flame, and many have a sweet aroma. The aromatic hydrocarbonsinclude benzene (C₆H₆) and derivatives of benzene, as well aspolyaromatic hydrocarbons.

Resins are the most polar and aromatic species present in thedeasphalted oil and, it has been suggested, contribute to the enhancedsolubility of asphaltenes in crude oil by solvating the polar andaromatic portions of the asphaltenic molecules and aggregates.

Asphaltenes are insoluble in n-alkanes (such as n-pentane or n-heptane)and soluble in toluene. The C:H ratio is approximately 1:1.2, dependingon the asphaltene source. Unlike most hydrocarbon constituents,asphaltenes contain a few percent of other atoms (called heteroatoms),such as sulfur, nitrogen, oxygen, vanadium, and nickel. Heavy oils andtar sands contain much higher proportions of asphaltenes than domedium-API oils or light oils. Condensates are virtually devoid ofasphaltenes. As far as asphaltene structure is concerned, experts agreethat some of the carbon and hydrogen atoms are bound in ring-like,aromatic groups, which also contain the heteroatoms. Alkane chains andcyclic alkanes contain the rest of the carbon and hydrogen atoms and arelinked to the ring groups. Within this framework, asphaltenes exhibit arange of molecular weight and composition. Asphaltenes have been shownto have a distribution of molecular weight in the range of 300 to 1400g/mol with an average of about 750 g/mol. This is compatible with amolecule contained seven or eight fused aromatic rings, and the rangeaccommodates molecules with four to ten rings. It is also known thatasphaltene molecules aggregate to form nanoaggregates and clusters.

Reservoir compartmentalization can be an impediment to efficientreservoir development. Reservoir compartmentalization is the naturaloccurrence of hydraulically isolated pockets within a reservoir. Inorder to produce a reservoir in an efficient manner, it is useful toknow the structure of the rock and the level of compartmentalization. Areservoir compartment does not produce unless it is tapped by a well. Inorder to justify the drilling of a well, the reservoir compartmentshould be sufficiently large to sustain economic production.Furthermore, in order to achieve efficient recovery, it is generallydesirable to know the locations of as many of the reservoir compartmentsas practical before extensive development has been done.

There are three industry standard procedures widely used to understandreservoir compartmentalization. First is the evaluation of petrophysicallogs. Petrophysical logs may identify impermeable barriers, and theexistence of such barriers can be taken to mean that the reservoir iscompartmentalized. Examples include gamma ray and NMR logs, both ofwhich can identify impermeable barriers in favorable situations. Anotherexample is the evaluation of mud filtrate invasion monitored byresistivity logs. However, impermeable barriers may be so thin that theyare not observable by these logs, or barriers observed by these logs maynot extend away from the wellbore and therefore may not compartmentalizethe reservoir. Second is the evaluation of pressure gradients. If twopermeable zones are not in pressure communication, they are not in flowcommunication. However, the presumption that pressure communicationimplies flow communication has been repeatedly proven to be incorrect.Pressure equilibration uses relatively little fluid flow and can occurmore than five orders of magnitude faster than fluid compositionalequilibration, even in the presence of flow barriers. Continuouspressure gradients are a necessary but insufficient test for reservoirconnectivity. Third is the comparison of geochemical fingerprints offluid samples acquired from different locations in the reservoir.Petroleum is a complex chemical mixture, containing many differentchemical compounds; the composition of that petroleum can therefore betreated as a fingerprint. If the composition of petroleum samples fromtwo different places in the reservoir is the same, it is assumed thatfluids can flow readily between those two places in the reservoir, andhence that the reservoir is connected. However, forces such asbiodegradation and water washing can occur to different extents indifferent parts of the reservoir, causing two locations in the reservoirto have different fingerprints even if they are connected. Additionally,petroleum samples generated from the same source rock may have verysimilar fingerprints even if they come from locations in the reservoirthat are presently disconnected.

An alternative method to assess connectivity is to evaluate hydrocarbonfluid compositional grading. The chemical composition of petroleum isdifferent in different parts of a connected reservoir. This change incomposition with position (such as with depth) in the reservoir isreferred to as compositional grading. The magnitude of thiscompositional grading (i.e., the difference in the composition of twofluids collected from different depths), in connected reservoirs atthermodynamic equilibrium, can be measured with downhole fluid analysisand predicted with a mathematical equation of state (EOS) model. The EOSmodel is based on assumptions that the reservoir is connected and atthermodynamic equilibrium. If the magnitude of compositional grading asmeasured matches the predicted composition grading, then the assumptionsof the EOS model are confirmed. In the event that the magnitude of themeasured compositional grading does not match the predictions of the EOSmodel, it can be assumed that there is reservoir compartmentalization orthat the reservoir fluids are not in equilibrium. Many different forcescan contribute to a lack of thermodynamic equilibrium, such as tar mats,water washing, biodegradation, and real-time charging. It can bedifficult to determine whether the reservoir is compartmentalized or ina state of thermodynamic non-equilibrium (e.g., disequilibrium), andthis determination can be useful in development decisions.

More specifically, there is an increasing awareness that fluids areoften heterogeneous in the reservoir and that reservoir fluidsfrequently demonstrate complicated fluid compositions, properties, andphase behaviors in single oil columns due to a variety of factorsincluding gravity, thermal gradients, biodegradation, active charging,water washing, and phase transitions. Most of these mechanisms result innon-equilibrium or non-stationary state conditions acting on reservoirfluids and, often, these non-equilibrium factors dominate over diffusiveand convective processes that can drive the fluids towards equilibrium.In these scenarios, the current modeling methods can be inaccurate andoffer limited insight into the real compositional properties of thereservoir fluids. These limitations make it difficult to determinewhether the reservoir is compartmentalized or connected, but in a stateof non-equilibrium.

Understanding the distribution of asphaltene content in oil and inorganic solids in reservoirs is useful for handling major productionconcerns, such as viscosity, flow assurance, reservoir connectivity, andtar/bitumen deposition. Advances in asphaltene science have enabledmodeling of asphaltene gradients from first principles. In addition,downhole fluid analysis (DFA) provides an effective measurement ofreservoir fluid gradients. In the disclosed embodiments, thiscombination of DFA measurement, the Yen-Mullins model of asphaltene, andthe Flory-Huggins-Zuo (FHZ) equation of state (EOS) provides a unifiedworkflow that has wide-ranging applicability.

The FHZ EOS quantitatively predicts the thermodynamic end state of aconnected reservoir in the vertical dimension. However, if the DFAmeasurements and the concentration gradient predicted by the FHZ EOS donot match, then either the reservoir is not connected, or there aresubsequent fluid migration processes ongoing and the reservoir simplyhas not had enough time to reach equilibrium. Many oil reservoirsexhibit thermodynamically equilibrated crude oils which are accuratelyrepresented by the FHZ EOS. However, there are many reservoirs which areundergoing dynamic processes in geologic time. The existingmethodologies do not take into account the time variable. In addition,the formulism is established in the one-dimensional (1D) verticaldimension, thus horizontal gradients are not accounted for in reservoirsin disequilibrium. Thus, the capability to simulate asphaltenedisequilibrium in connected reservoirs is highly useful.

The disclosed embodiments include a methodology which includes a DFAprediction workflow, and is capable of simulating the asphaltenedynamical segregation in geologic time in three dimensions (3D). Themodel used in the disclosed embodiments is established based on thegeneral theory of thermodynamics of multicomponent mixtures in porousmedia, combined with the Flory-Huggins regular solution model ofasphaltenes. In addition, the disclosed model takes into account 4distinct mechanisms including Darcy's law, molecular diffusion,gravitational diffusion (gravity segregation), and thermal diffusion. Itis noted that the gravitational diffusion is taken into account in thedisclosed method because gravitational diffusion is the most pronouncedmechanism for the heavy end of crude oil in reservoirs.

Using the disclosed methods combined with the DFA measurements enablesassessment of whether the reservoir is connected but not in equilibriumor if the reservoir is compartmentalized. The disclosed simulationworkflow thus aids in determining both compositional equilibrium anddisequilibrium, reservoir compartmentalization, as well as correctlyinitializing reservoir production simulation, reserve estimation, andfield development plan (FDP) strategies.

In certain embodiments, the reservoir characterization may includeperforming several steps. For example, a first step may include using anumerical model to simulate over geological time a non-equilibriumconcentration of an asphaltene component as a function of locationwithin the wellbore. A second step may include analyzing fluid samplesacquired from at least one wellbore that traverses the reservoir ofinterest to measure concentration of the asphaltene component as afunction of location within the wellbore. A third step may includecomparing the non-equilibrium concentration of the asphaltene componentas a function of location within the wellbore resulting from thesimulation of the first step to the concentration of the asphaltenecomponent as a function of location within the wellbore as measured inthe second step. A fourth step may include characterizing the reservoirof interest based upon the comparing of the third step.

FIG. 1A illustrates an embodiment of a tool 10 for petroleum reservoirdownhole fluid analysis that can be used as part of a workflow forreservoir analysis in accordance with present embodiments. The tool 10is suspended in the borehole 12 from the lower end of a multiconductorcable 15 that is spooled in a usual fashion on a suitable winch on theearth's surface. The cable 15 is electrically coupled to an electricalcontrol system 18 on the earth's surface. The tool 10 includes anelongated body 19 which carries a selectively extendable fluid admittingassembly 20 and a selectively extendable tool anchoring member 21 whichare respectively arranged on opposite sides of the tool body 19. Thefluid admitting assembly 20 is equipped for selectively sealing off orisolating selected portions of the wall of the borehole 12 such thatfluid communication with the adjacent earth formation 14 is established.The fluid admitting assembly 20 and tool 10 include a flowline leadingto a fluid analysis module 25. The formation fluid obtained by the fluidadmitting assembly 20 flows through the flowline and through the fluidanalysis module 25. The fluid may thereafter be expelled through a portor it may be sent to one or more fluid collecting chambers 22 and 23which may receive and retain the fluids obtained from the formation.With the fluid admitting assembly 20 sealingly engaging the formation14, a short rapid pressure drop can be used to break the mudcake seal.Normally, the first fluid drawn into the tool 10 will be highlycontaminated with mud filtrate. As the tool 10 continues to draw fluidfrom the formation 14, the area near the fluid admitting assembly 20cleans up and reservoir fluid becomes the dominant constituent. The timefor cleanup depends upon many parameters, including formationpermeability, fluid viscosity, the pressure differences between theborehole and the formation, and overbalanced pressure difference and itsduration during drilling. Increasing the pump rate can shorten thecleanup time, but the rate is controlled carefully to preserve formationpressure conditions.

The fluid analysis module 25 includes means for measuring thetemperature and pressure of the fluid in the flowline. The fluidanalysis module 25 derives properties that characterize the formationfluid sample at the flowline pressure and temperature. In certainembodiments, the fluid analysis module 25 measures absorption spectraand translates such measurements into concentrations of several alkanecomponents and groups (or lumps) in the fluid sample. In an illustrativeembodiment, the fluid analysis module 25 provides measurements of theconcentrations (e.g., weight percentages) of carbon dioxide (CO₂),methane (CH₄), ethane (C₂H₆), the C3-C5 alkane group, the lump of hexaneand heavier alkane components (C6+), and asphaltene content. The C3-C5alkane group includes propane, butane, and pentane. The C6+ alkane groupincludes hexane (C₆H₁₄), heptane (C₇H₁₆), octane (C₈H₁₈), nonane(C₉H₂₀), decane (C₁₀H₂₂), hendecane (C₁₁H₂₄)—also referred to asendecane or undecane, dodecane (C₁₂H₂₆), tridecane (C₁₃H₂₈), tetradecane(C₁₄H₃₀), pentadecane (C₁₅H₃₂), hexadecane (C₁₆H₃₄), etc. The fluidanalysis module 25 also provides a means that measures live fluiddensity (ρ) at the flowline temperature and pressure, live fluidviscosity (μ) at flowline temperature and pressure (in cp), formationpressure, and formation temperature.

Control system 18 maintains control of the fluid admitting assembly 20and fluid analysis module 25 and the flow path to the fluid collectingchambers 22, 23. The fluid analysis module 25 and the surface-locatedelectrical control system 18 may include data processing functionality(e.g., one or more microprocessors, associated memory, and otherhardware and/or software) to implement the disclosed embodiments asdescribed herein. The electrical control system 18 can also be realizedby a distributed data processing system wherein data measured by thetool 10 is communicated (such as in real time) over a communication link(such as a satellite link) to a remote location for data analysis asdescribed herein. The data analysis can be carried out on a workstationor other suitable data processing system (such as a computer cluster orcomputing grid).

Formation fluids sampled by the tool 10 may be contaminated with mudfiltrate. That is, the formation fluids may be contaminated with thefiltrate of a drilling fluid that seeps into the formation 14 during thedrilling process. Thus, when fluids are withdrawn from the formation 14by the fluid admitting assembly 20, they may include mud filtrate. Insome examples, formation fluids are withdrawn from the formation 14 andpumped into the borehole or into a large waste chamber in the tool 10until the fluid being withdrawn becomes sufficiently clean. A cleansample is one where the concentration of mud filtrate in the samplefluid is acceptably low so that the fluid substantially representsnative (i.e., naturally occurring) formation fluids. In the illustratedexample, the tool 10 is provided with fluid collecting chambers 22 and23 to store collected fluid samples.

The tool 10 of FIG. 1A is adapted to make in situ determinationsregarding hydrocarbon-bearing geological formations by downhole samplingof reservoir fluid at one or more measurement stations within theborehole 12, and by conducting downhole fluid analysis of one or morereservoir fluid samples for each measurement station (includingcompositional analysis such as estimating concentrations of a pluralityof compositional components of a given sample and other fluidproperties).

FIG. 1B illustrates an embodiment of the fluid analysis module 25 ofFIG. 1A (labeled 25′), including a probe 202 having a port 204 to admitformation fluid therein. A hydraulic extending mechanism 206 may bedriven by a hydraulic system 220 to extend the probe 202 to sealinglyengage the formation 14. In alternative implementations, more than oneprobe can be used, or inflatable packers can replace the probe(s) andfunction to establish fluid connections with the formation and samplefluid samples.

The probe 202 can be realized by the Quicksilver Probe available fromSchlumberger Technology Corporation of Sugar Land, Tex., USA. In otherembodiments, the probe 202 may be replaced or supplemented with othertypes of suitable probes. The Quicksilver Probe divides the fluid flowfrom the reservoir into two concentric zones, a central zone isolatedfrom a guard zone about the perimeter of the central zone. The two zonesare connected to separate flowlines with independent pumps. The pumpscan be run at different rates to exploit filtrate/fluid viscositycontrast and permeability anistrotropy of the reservoir. Higher intakevelocity in the guard zone directs contaminated fluid into the guardzone flowline, while clean fluid is drawn into the central zone. Fluidanalyzers analyze the fluid in each flowline to determine thecomposition of the fluid in the respective flowlines. The pump rates canbe adjusted based on such compositional analysis to achieve and maintaindesired fluid contamination levels. The operation of the QuicksilverProbe efficiently separates contaminated fluid from cleaner fluid earlyin the fluid extraction process, which results in obtaining clean fluidin much less time compared to traditional formation testing tools.

The fluid analysis module 25′ includes a flowline 207 that carriesformation fluid from the port 204 through a fluid analyzer 208. Thefluid analyzer 208 includes a light source that directs light to asapphire prism disposed adjacent the flowline fluid flow. The reflectionof such light is analyzed by a gas refractometer and dual fluoroscenedetectors. The gas refractometer qualitatively identifies the fluidphase in the flowline. At the selected angle of incidence of the lightemitted from the diode, the reflection coefficient is much larger whengas is in contact with the window than when oil or water is in contactwith the window. The dual fluoroscene detectors detect free gas bubblesand retrograde liquid dropout to accurately detect single phase fluidflow in the flowline 207. Fluid type is also identified. The resultingphase information can be used to define the difference betweenretrograde condensates and volatile oils, which can have similar gas-oilratios (GORs) and live oil densities. It can also be used to monitorphase separation in real time and ensure single phase sampling. Thefluid analyzer 208 also includes dual spectrometers—a filter arrayspectrometer and a grating-type spectrometer.

The filter array spectrometer of the analyzer 208 includes a broadbandlight source providing broadband light that passes along optical guidesand through an optical chamber in the flowline 207 to an array ofoptical density detectors that are designed to detect narrow frequencybands (commonly referred to as channels) in the visible andnear-infrared spectra as described in U.S. Pat. No. 4,994,671,incorporated herein by reference in its entirety. These channels includea subset of channels that detect water absorption peaks (which are usedto characterize water content in the fluid) and a dedicated channelcorresponding to the absorption peak of CO₂ with dual channels above andbelow this dedicated channel that subtract out the overlapping spectrumof hydrocarbon and small amounts of water (which are used tocharacterize CO₂ content in the fluid). The filter array spectrometeralso employs optical filters that provide for identification of thecolor (also referred to as “optical density” or “OD”) of the fluid inthe flowline. Such color measurements support fluid identification,determination of asphaltene content and pH measurement. Mud filtrates orother solid materials generate noise in the channels of the filter arrayspectrometer. Scattering caused by these particles is independent ofwavelength. In certain embodiments, the effect of such scattering can beremoved by subtracting a nearby channel.

The grating-type spectrometer of the fluid analyzer 208 is designed todetect channels in the near-infrared spectra (such as between 1600 and1800 nm) where reservoir fluid has absorption characteristics thatreflect molecular structure.

The fluid analyzer 208 also includes a pressure sensor for measuringpressure of the formation fluid in the flowline 207, a temperaturesensor for measuring temperature of the formation fluid in the flowline207, and a density sensor for measuring live fluid density of the fluidin the flowline 207. In certain embodiments, the density sensor isrealized by a vibrating sensor that oscillates in two perpendicularmodes within the fluid. Simple physical models describe the resonancefrequency and quality factor of the sensor in relation to live fluiddensity. Dual mode oscillation is advantageous over other resonanttechniques because it minimizes the effects of pressure and temperatureon the sensor through common mode rejection. In addition to density, thedensity sensor can also provide a measurement of live fluid viscosityfrom the quality factor of oscillation frequency. Note that live fluidviscosity can also be measured by placing a vibrating object in thefluid flow and measuring the increase in line width of any fundamentalresonance. This increase in line width is related closely to theviscosity of the fluid. The change in frequency of the vibrating objectis closely associated with the mass density of the object. If density ismeasured independently, then the determination of viscosity is moreaccurate because the effects of a density change on the mechanicalresonances are determined. Generally, the response of the vibratingobject is calibrated against known standards. The fluid analyzer 208 canalso measure resistivity and pH of fluid in the flowline 207. In certainembodiments, the fluid analyzer 208 is realized by the InSitu FluidAnalyzer available from Schlumberger Technology Corporation. In otherembodiments, the flowline sensors of the fluid analyzer 208 may bereplaced or supplemented with other types of suitable measurementsensors (e.g., NMR sensors and capacitance sensors). Pressure sensor(s)and/or temperature sensor(s) for measuring pressure and temperature offluid drawn into the flowline 207 can also be part of the probe 202.

A pump 228 is fluidly coupled to the flowline 207 and is controlled todraw formation fluid into the flowline 207 and to supply formation fluidto the fluid collecting chambers 22 and 23 (FIG. 1A) via valve 229 andflowpath 231 (FIG. 1B).

The fluid analysis module 25′ includes a data processing system 213 thatreceives and transmits control and data signals to the other componentsof the module 25′ for controlling operations of the module 25′. The dataprocessing system 213 also interfaces to the fluid analyzer 208 forreceiving, storing, and processing the measurement data generatedtherein. In the certain embodiments, the data processing system 213processes the measurement data output by the fluid analyzer 208 toderive and store measurements of the hydrocarbon composition of fluidsamples analyzed in situ by the fluid analyzer 208, including: flowlinetemperature; flowline pressure; live fluid density (ρ) at the flowlinetemperature and pressure; live fluid viscosity (μ) at flowlinetemperature and pressure; concentrations (e.g., weight percentages) ofcarbon dioxide (CO₂), methane (CH₄), ethane (C₂H₆), the C3-C5 alkanegroup, the lump of hexane and heavier alkane components (C6+), andasphaltene content; GOR; and possibly other parameters (such as APIgravity and oil formation volume factor (Bo)).

Flowline temperature and pressure are measured by the temperature sensorand pressure sensor, respectively, of the fluid analyzer 208 (and/orprobe 202). In one embodiment, the output of the temperature sensor(s)and pressure sensor(s) are monitored continuously before, during, andafter sample acquisition to derive the temperature and pressure of thefluid in the flowline 207. The formation temperature is not likely todeviate substantially from the flowline temperature at a givenmeasurement station and thus can be estimated as the flowlinetemperature at the given measurement station in many applications.Formation pressure can be measured by the pressure sensor of the fluidanalyzer 208 in conjunction with the downhole fluid sampling andanalysis at a particular measurement station after buildup of theflowline to formation pressure.

Live fluid density (ρ) at the flowline temperature and pressure isdetermined by the output of the density sensor of the fluid analyzer 208at the time the flowline temperature and pressure are measured.

Live fluid viscosity (μ) at flowline temperature and pressure is derivedfrom the quality factor of the density sensor measurements at the timethe flowline temperature and pressure are measured.

The measurements of the hydrocarbon composition of fluid samples arederived by translation of the data output by spectrometers of the fluidanalyzer 208.

The GOR is determined by measuring the quantity of methane and liquidcomponents of crude oil using near-infrared absorption peaks. The ratioof the methane peak to the oil peak on a single phase live crude oil isdirectly related to GOR.

The fluid analysis module 25′ can also detect and/or measure other fluidproperties of a live oil sample, including retrograde dew formation,asphaltene precipitation, and/or gas evolution.

The fluid analysis module 25′ also includes a tool bus 214 thatcommunicates data signals and control signals between the dataprocessing system 213 and the surface-located control system 18 of FIG.1A. The tool bus 214 can also carry electrical power supply signalsgenerated by a surface-located power source for supply to the fluidanalysis module 25′, and the module 25′ can include a power supplytransformer/regulator 215 for transforming the electric power supplysignals supplied via the tool bus 214 to appropriate levels suitable foruse by the electrical components of the module 25′.

Although the data processing components of FIG. 1B are shown anddescribed above as being communicatively coupled and arranged in aparticular configuration, the components of the fluid analysis module25′ can be communicatively coupled and/or arranged differently thandepicted in FIG. 1B without departing from the scope of the presentdisclosure. In addition, the example methods, apparatus, and systemsdescribed herein are not limited to a particular conveyance type but,instead, may be implemented in connection with different conveyancetypes including, for example, coiled tubing, wireline, wired drill pipe,and/or other conveyance means known in the industry.

In accordance with the disclosed embodiments, the tool 10 of FIGS. 1Aand 1B can be employed as part of the methodology 250 of FIGS. 2A-2C toevaluate a petroleum reservoir of interest. The surface-locatedelectrical control system 18 and the fluid analysis module 25 of thetool 10 each include data processing functionality (e.g., one or moremicroprocessors, associated memory, and other hardware and/or software)that cooperate to implement the method 250 and embodiments as describedherein. Specifically, the control system 18 and/or the module 25 mayinclude machine readable instructions disposed on a memory device (e.g.,stored within circuitry of the control system 18 and/or module 25 orwithin a separate memory or other tangible readable medium) and theinstructions may monitor or control operations of the downhole tool 10to implement the method 250. The electrical control system 18 can alsobe realized by a distributed data processing system wherein datameasured by the tool 10 is communicated in real time over acommunication link (such as a satellite link) to a remote location fordata analysis as described herein. The data analysis can be carried outon a workstation or other suitable data processing system (such as acomputer cluster or computing grid).

The operations of FIGS. 2A-2C begin in step 252 by employing the tool 10of FIGS. 1A and 1B to obtain a sample of the formation fluid at thereservoir pressure and temperature (a live oil sample) at a measurementstation in the wellbore (for example, a reference station), and thesample is processed in the downhole environment by the fluid analysismodule 25. In the certain embodiments, the fluid analysis module 25conducts downhole fluid analysis (DFA) of the formation fluid byperforming spectrophotometry measurements that measure absorptionspectra of the live oil sample and translates such spectrophotometrymeasurements into concentrations of several alkane components and groups(or lumps) in the fluids of interest. In an illustrative embodiment, thefluid analysis module 25 provides measurements of the concentrations(e.g., weight percentages) of carbon dioxide (CO₂), methane (CH₄),ethane (C₂H₆), the C3-C5 alkane group including propane, butane,pentane, the lump of hexane and heavier alkane components (C6+), andasphaltene content. The tool 10 also provides a means to measuretemperature of the fluid sample (and thus reservoir temperature at thestation), pressure of the fluid sample (and thus reservoir pressure atthe station), live fluid density of the fluid sample, live fluidviscosity of the fluid sample, gas-oil ratio (GOR) of the fluid sample,optical density, and possibly other fluid properties (such as APIgravity, formation volume fraction (Bo), retrograde dew formation,asphaltene precipitation, and gas evolution) of the fluid sample.

As part of step 252, the tool 10 can also be controlled to collect andstore one or more isolated live oil samples in fluid collecting chambers22, 23 (FIG. 1A) of the tool 10. The respective live oil sample iscollected at reservoir conditions (at the formation temperature andpressure) for the measurement station and stored within a sealed samplecontainer at these conditions for transport uphole to the wellsite whenthe tool is withdrawn from the wellbore.

As part of step 252, isolated core samples can also be acquired by thetool 10 and stored within the tool 10 for transport uphole to thewellsite when the tool is withdrawn from the wellbore. Alternatively, aseparate coring tool can be used to acquire isolated core sample fromthe wellbore. There are several types of core samples that can berecovered from the wellbore, including full diameter cores, orientedcores, native state cores, and sidewall cores. Coring operations can berun in combination with other suitable logging operations (such as gammaray logging) to correlate with openhole logs for accurate, real timedepth control of the coring points.

In step 256, the DFA FHZ workflow is used to evaluate whether thereservoir is both connected and in equilibrium. A number of referencesdisclose the use of the FHZ methodology, such as, but not limited to,U.S. Pat. No. 7,996,154, U.S. Publication No. 2009/0312997, U.S.Publication No. 2012/0296617, U.S. Publication No. 2014/0200810, each ofwhich is incorporated herein by reference in its entirety. In certainembodiments, the FHZ EOS may be expressed as follows:

$\begin{matrix}{\frac{{OD}\left( h_{2} \right)}{{OD}\left( h_{1} \right)} = {\frac{\phi_{a}\left( h_{2} \right)}{\phi_{a}\left( h_{1} \right)} = {\exp\left\{ {\frac{v_{a}{g\left( {\rho - \rho_{a}} \right)}\left( {h_{2} - h_{1}} \right)}{RT} + \left. \quad{{\frac{v_{a}}{RT}\left\lbrack {\left( {\delta_{a} - \delta} \right)_{h_{1}}^{2} - \left( {\delta_{a} - \delta} \right)_{h_{2}}^{2}} \right\rbrack} + \left\lbrack {\left( \frac{v_{a}}{v} \right)_{h_{2}} - \left( \frac{v_{a}}{v} \right)_{h_{1}}} \right\rbrack} \right\}} \right.}}} & (1)\end{matrix}$where

ϕ_(a)(h₁) is the volume fraction for asphaltene at depth h₁,

ϕ_(a)(h₂) is the volume fraction for asphaltene at depth h₂,

OD(h₁) is the optical density for asphaltene at depth h₁,

OD(h₂) is the optical density for asphaltene at depth h₂,

ν_(a) is the partial molar volume for the asphaltene,

ν is the molar volume for the bulk fluid,

g is the gravitational acceleration,

δ_(a) is the solubility parameter for the asphaltene,

δ is the solubility parameter for the bulk fluid,

ρ_(a) is the density for the asphaltene,

ρ is the density for the bulk fluid,

R is the universal gas constant, and

T is the absolute temperature of the reservoir fluid.

The equation of state (EOS) model describes the thermodynamic behaviorof the fluid and provides for characterization of the reservoir fluid atdifferent locations within the reservoir. With the reservoir fluidcharacterized with respect to its thermodynamic behavior, fluidproduction parameters, transport properties, and other commerciallyuseful indicators of the reservoir can be computed.

For example, the EOS model can provide the phase envelope that can beused to interactively vary the rate at which samples are collected inorder to avoid entering the two phase region. In another example, theEOS can provide useful properties in assessing production methodologiesfor the reservoir. Such properties can include density, viscosity, andvolume of gas formed from a liquid after expansion to a specifiedtemperature and pressure. The characterization of the fluid sample withrespect to its thermodynamic model can also be used as a benchmark todetermine the validity of the obtained sample, whether to retain thesample, and/or whether to obtain another sample at the location ofinterest. More particularly, based on the thermodynamic model andinformation regarding formation pressures, sampling pressures, andformation temperatures, if it is determined that the fluid sample wasobtained near or below the bubble point line of the sample, a decisionmay be made to jettison the sample and/or to obtain another sample at aslower rate (i.e., a smaller pressure drop) so that gas will not evolveout of the sample. Alternatively, because knowledge of the dew point ofa retrograde gas condensate in a formation is desirable, a decision maybe made (when conditions allow) to vary the pressure drawdown in anattempt to observe the liquid condensation and thus establish the actualsaturation pressure.

For example, if the DFA measurements and the vertical concentrationgradient predicted by the FHZ EOS do not match, then either thereservoir is not connected, or there are subsequent fluid migrationprocesses ongoing and the reservoir has not had enough time to reachequilibrium. In addition, if the results of the DFA FHZ workflowindicate a horizontal gradient, then either the reservoir is notconnected or in disequilibrium. If either situation (e.g., verticalgradient not matching or horizontal gradient), then the method 250proceeds to step 258. Otherwise, the method 250 proceeds to step 254,which indicates that the reservoir is likely connected.

Steps 258, 260, and 262 of the method 250 are conducted in preparationfor simulating the dynamic distribution of asphaltene in the reservoir.Specifically, in step 258, reservoir properties based on the DFAmeasurement results are set up. Examples of these reservoir propertiesinclude, but are not limited to, depth, length, permeability, porosity,and viscosity. In step 260, the initial condition of the simulationbased on the petroleum system modeling results is set up. In step 262,the boundary condition of the simulation based on the geochemistryresults is set up. For example, if there is no gas charge present in thereservoir, then a zero flux boundary condition is used. Otherwise, a gasinflux boundary condition is used in step 262.

In step 264, the dynamic distribution of asphaltene with time issimulated using a dynamic asphaltene distribution model, the derivationof which is described in detail below. The general governing equationsfor a multicomponent fluid system are as follows. Darcy's velocity of anaverage fluid is given by:

$\begin{matrix}{u = {{- \frac{k}{\phi\mu}}\left( {{\Delta\; P} - {\rho\; g\;{\nabla z}}} \right)}} & (2)\end{matrix}$where u is the pore velocity vector, Φ is the porosity, k, μ, P, ρ arethe fluid permeability, viscosity, pressure, and mass density,respectively, g is the gravitational constant, z is the depth pointingdownward, and ∇ is the gradient operator. The conservation of mass(without source/sink term) is given by:

$\begin{matrix}{{\frac{\partial\rho}{\partial t} + {\nabla{\cdot \left( {\rho\; u} \right)}}} = 0} & (3)\end{matrix}$where ∇ is the divergence operator,

${\nabla{\cdot u}} = {\frac{\partial u_{1}}{\partial x_{1}} + \frac{\partial u_{2}}{\partial x_{2}} + {\frac{\partial u_{3}}{\partial x_{3}}.}}$The mass conservation of each component is expressed as:

$\begin{matrix}{{{\frac{\partial\left( {w_{i}\rho} \right)}{\partial t} + {\nabla{\cdot \left( {{w_{i}\rho\; u} + J_{i}} \right)}}} = 0},{i = 1},\ldots\;,N_{C}} & (4)\end{matrix}$where w_(i) is the weight fraction of component i. The mass flux J_(i)vector has 3 terms: molecular diffusion, gravity diffusion, andtemperature diffusion:J _(i)=−ρ(Σ_(j) D _(ij) ^(M) ∇x _(j) +D _(i) ^(G) ∇P+D _(i) ^(T)∇T)  (5)where D_(ij) ^(M), D_(i) ^(G), and D_(i) ^(T) are the correspondingdiffusion tensors, the latter two are diagonal, and x_(i) is the molefraction of component i.

Equations (2) to (5) complete the general governing equations that cansolve a large variety of multicomponent mixture transportation problems,which takes into account four distinct mechanisms that impact thecomposition variation: (a) molecular diffusion is the tendency to mixfluids due to concentration gradient; (b) gravity diffusion is thetendency to separate components with different molar mass due to earth'sgravity, so that heavier components segregate towards the bottom andlighter components segregate towards the top; (c) thermal diffusion isthe counter effect of gravity, with the tendency to separate componentsdue to earth's geothermal gradient, since temperature is a measure ofthe kinetic energy of molecules, in an ideal mixture lighter moleculesfind it easier to move towards regions with lower temperature; and (d)convection is the convective flow caused by density gradient. Thegravity diffusion and thermal diffusion mainly affect the verticalgradient, while convection mainly affects the horizontal gradient.

Although the diffusive flux J_(i) has a general formulism, it includesthe phenomenological coefficients and is difficult to be analyzed for aspecific reservoir problem. To assess the asphaltene gradientdistribution with time, a composition lumping strategy is used. Thereservoir fluid can be treated as a mixture of two groups ofmulticomponents: a solvent group (non-asphaltene components or maltene)and a solute group (asphaltenes). Of course, the solvent is also amixture whose properties are calculated by an EOS. In the followingdiscussion, we restrict our study to a binary mixture, however, theapplication of the disclosed method to binary mixtures is a non-limitingexample and the disclosed method may be used for various multicomponentmixture systems as well. The subscripts 1 or 2 represent the propertycorresponding to component 1 or component 2. For such case theasphaltene diffusive flux is expressed as:

$\begin{matrix}{J_{1} = {{- \frac{M_{1}M_{2}}{M^{2}}}D_{12}{\rho\left( {\frac{{\partial\ln}\; f_{1}}{{\partial\ln}\; x_{1}}❘_{P,T}{{\nabla x_{1}} + {\frac{x_{1}}{RT}\left( {\frac{M_{1}}{\rho} - {\overset{\_}{V}}_{1}} \right){\nabla P}} + {K_{T}{\nabla\;\ln}\; T}}} \right)}}} & (6)\end{matrix}$where M and M_(i) represents molecular weight, D₁₂ is the moleculardiffusion coefficient of components 1 and 2, f is the fugacity, V ₁ isthe partial molar volume of component 1, and K_(T) is the thermaldiffusion ratio, respectively. For an isothermal system, the temperaturegradient term vanishes; and for large species like asphaltenes, V ₁≈V₁which is the asphaltene molar volume. Denoting the density differenceΔρ=ρ₁−ρ, combining equations (4) and (6) yields the conservation ofcomponent 1:

$\begin{matrix}{{\frac{\partial\left( {w_{1}\rho} \right)}{\partial t} + {\nabla{\cdot \left( {{w_{1}\rho\; u} - {\frac{M_{1}M_{2}}{M^{2}}D_{12}{\rho\left( {\frac{{\partial\ln}\; f_{1}}{{\partial\ln}\; x_{1}}❘_{P,T}{{\nabla x_{1}} + {\frac{\Delta\;\rho\; x_{1}v_{1}}{\rho\; R\; T}{\nabla P}}}} \right)}}} \right)}}} = 0} & (7)\end{matrix}$The mixture molar mass M, density ρ (thus Δρ) and component weightfraction w₁ can be expressed in terms of mole fraction x₁ and molarvolume V:

$\begin{matrix}{{M = {{\left( {M_{1} - M_{2}} \right)x_{1}} + M_{2}}},{w_{1} = \frac{x_{1}M_{1}}{M}}} & (8) \\{{\rho = \frac{M}{V}},{{\Delta\;\rho} = {\rho_{1} - \frac{M}{V}}}} & (9)\end{matrix}$Thus, equation (7) can be expressed using molar properties as:

$\begin{matrix}{{\frac{\partial\left( {x_{1}/V} \right)}{\partial t} + {\nabla{\cdot \left( {\frac{x_{1}u}{V} - {\frac{M_{2}}{M\; V}{D_{12}\left( {\frac{{\partial\ln}\; f_{1}}{{\partial\ln}\; x_{1}}❘_{P,T}{{\nabla x_{1}} + {\frac{\Delta\;\rho\; x_{1}V_{1}}{\rho\; R\; T}{\nabla P}}}} \right)}}} \right)}}} = 0} & (10)\end{matrix}$

For a non-ideal solution, such as most reservoir fluids, the issue thatremains is to evaluate fugacity using an EOS. Particularly forasphaltenes, it can be evaluated by the multicomponent Flory-Hugginsregular solution model:

$\begin{matrix}{\frac{\left. {d\;\mu} \right|_{P,T}}{R\; T} = {\left. {d\;\ln\; f_{1}} \right|_{P,T} = {d\left( {{\ln\;\varnothing_{1}} - \frac{V_{1}}{V} + \frac{{V_{1}\left( {\delta_{1} - \delta} \right)}^{2}}{R\; T}} \right)}}} & (11)\end{matrix}$where Ø₁ is the asphaltene volume fraction, δ and δ₁ are mixture andasphaltene solubility. The asphaltene and solvent solubility are definedby:

$\begin{matrix}{{\delta_{1} = \sqrt{A\;\rho}},{\delta_{2} = \sqrt{\frac{{\Delta\; H^{vap}} - {R\; T}}{V_{2}}}}} & (12)\end{matrix}$where A is the monomer heat of vaporization in units of kJ/g andΔH^(vap) is the heat of vaporization as reported in the literature,respectively. Values for the asphaltene solubility can be estimated by,e.g. δ₁=21.85(1−1.07×10⁻³(T−T₀)) MPa^(0.5) where T₀ is room temperature,and for bulk oil, δ may vary from 10 to 17 MPa^(0.5) depending onreservoir oil type. The mixture solubility can be estimated by, e.g.δ=17.347ρ+2.904 based on lab correlations. More detailed analysis andconsiderations on the magnitude and dependencies of the parameters andterms in the FHZ EOS, namely gravity, solubility, and entropy, aspresented by Freed, Mullins, and Zuo in “Heuristics for EquilibriumDistributions of Asphaltenes in the Presence of GOR Gradients,” EnergyFuels, Vol. 28, No. 8, pp. 4859-4869, 2014, may be used to aid inparameters determination. The mixing rule for volume fraction andsolubility is given by:

$\begin{matrix}{{\varnothing_{1} = \frac{x_{1}V_{1}}{{x_{1}V_{1}} + {x_{2}V_{2}}}},{\delta = {{\varnothing_{1}\delta_{1}} + {\left( {1 - \varnothing_{1}} \right)\delta_{2}}}}} & (13)\end{matrix}$With some derivative calculations using equations (9), (10), and (13),it is derived that:

$\begin{matrix}{{{\gamma^{F\; H} \equiv \frac{{\partial\ln}\; f_{1}}{{\partial\ln}\; x_{1}}}❘_{P,T}} = {\frac{V_{2}}{{x_{1}V_{1}} + {x_{2}V_{2}}} + {\frac{x_{1}V_{1}}{V^{2}}\frac{\partial V}{\partial x_{1}}} - \frac{2\; V_{1}V_{2}\varnothing_{1}{\varnothing_{2}\left( {\delta_{1} - \delta_{2}} \right)}^{2}}{R\;{T\left( {{x_{1}V_{1}} + {x_{2}V_{2}}} \right)}}}} & (14)\end{matrix}$

where γ^(FH) includes both the entropy and solubility terms that comefrom the non-ideality of reservoir fluids. The molar volume V and itsderivative can be calculated using a cubic EOS, such as Peng-Robinson1976, or Redlich-Kwong-Soave 1972, and volume deficiency can becorrected by the volume translation method (Peneloux 1982).Particularly, if additive volume is assumed, then the mixture molarvolume can be evaluated by V=(V₁−V₂)x₁+V₂, and

${\frac{\partial V}{\partial x_{1}} = {V_{1} - V_{2}}},$and in this case γ^(FH) becomes:

$\begin{matrix}{\gamma^{F\; H} = {1 + \frac{\left( {V_{1} - V} \right)\left( {V - V_{2}} \right)}{V^{2}} - \frac{2\; V_{1}V_{2}\varnothing_{1}{\varnothing_{2}\left( {\delta_{1} - \delta_{2}} \right)}^{2}}{R\; T\; V}}} & (15)\end{matrix}$which can be directly calculated without a cubic EOS. For the specialcase of an ideal solution, the fugacity is related with mole fraction bydlnf₁|_(P,T)=dlnx₁, in this case:γ^(FH)=1  (16)Thus, only the gravity term will be taken into account. Thissimplification can be used for heavy oil because in heavy oil, thegravity term dominates the other two terms. With the resolution ofγ^(FH), now equation (10) becomes:

$\begin{matrix}{{\frac{\partial\left( {x_{1}/V} \right)}{\partial t} + {\nabla{\cdot \left( {\frac{x_{1}u}{V} - {\frac{M_{2}}{M\; V}{D_{12}\left( {{\gamma^{F\; H}{\nabla x_{1}}} + {\frac{\Delta\;\rho\; x_{1}V_{1}}{\rho\; R\; T}{\nabla\; P}}} \right)}}} \right)}}} = 0} & (17)\end{matrix}$where the Darcy velocity u, mixture molar mass M, the density differenceΔρ and non-ideality terms γ^(FH) are given by equations (2), (8), (9),and (15). Equation (17) is referred to as the FHZ equation of dynamicsfor asphaltenes, which describes the dynamical process of asphaltenegravitational diffusion and advection. The driving forces are Darcy'sadvection velocity term, entropy term, solubility term, and gravityterm.

The relationship between the dynamical equation (17) and thethermodynamic FHZ EOS is that, at the equilibrium state, both the bulkvelocity u and diffusive flux J₁ in equation (17) become 0, whichyields:

$\begin{matrix}{\left. {\frac{1}{R\; T}\frac{\partial\;\mu}{\partial z}} \right|_{P,T} = {\left. \frac{{\partial\;\ln}\; f_{1}}{\partial z} \right|_{P,T} = {\frac{d\left( {{\ln\;\varnothing_{1}} - \frac{V_{1}}{V} + \frac{{V_{1}\left( {\delta_{1} - \delta} \right)}^{2}}{R\; T}} \right)}{\mathbb{d}\; z} = \frac{\Delta\;\rho\; g\; V_{1}}{R\; T}}}} & (18)\end{matrix}$

Equation (18) is equivalent to the FHZ EOS at equilibrium. However,using (17), the disequilibrium (i.e., the dynamic variation with time)can be resolved. Combining equation (17) with the mass conservation ofbulk fluid expressed by equation (3), the two variables to be solved arethe mole fraction x₁ and pressure P. By solving these two equations wecan simulate the composition variation with time in 3D. The initialcondition is set up based on petroleum system modeling results, and thezero mass flux boundary condition is used if there is no current gascharge. Otherwise a gas influx from the top can be used as boundarycondition. The molar properties of asphaltene in various crude oils isresolved by the Yen-Mullins model, and can be directly used in thesimulation. Note that viscosity is not assumed constant and itsvariation with time can also be simulated, e.g. using theLohrenz-Bray-Clark correlation (1964). The mixture molar volume V andthus mixture density is estimated by the cubic equation of state. Itshould be noted that if there is dramatic temperature gradient in thereservoir under evaluation, then the reservoir is by definition notequilibrated. In this case, the temperature term in equation (6) can beadded to the simulation to properly account for the thermal diffusion.

In step 266, the simulated disequilibrium distribution from step 264 iscompared with the DFA measurement results obtained in step 252. In step268, the difference from step 266 is compared with a threshold value toindicate whether the difference is small enough. If the difference issmall enough (e.g., the difference is less than the threshold value),then the reservoir is likely connected (step 276). In this situation,the simulation model may be tuned based on measurement uncertainties andthe method 250 repeated by going to step 258. Alternatively, if thedifference is large (e.g., the difference is greater than the thresholdvalue), then the reservoir is likely disconnected (step 270). Forexample, the reservoir may include a baffle or fault. In this situation,the connectivity issue may be cross validated with the geochemistryresults and lab measurements in step 272. In addition, detailed DFAmeasurements may be performed to determine the compartmentalizationlocation with specificity (step 274).

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method for characterizing a hydrocarbonreservoir of interest traversed by at least one wellbore, the methodcomprising: (a) using a numerical model to simulate over geological timea non-equilibrium concentration of an asphaltene component as a functionof location within the wellbore; (b) analyzing fluid samples acquiredfrom at least one wellbore that traverses the reservoir of interest tomeasure concentration of the asphaltene component as a function oflocation within the wellbore; (c) comparing the non-equilibriumconcentration of the asphaltene component as a function of locationwithin the wellbore resulting from the simulation of (a) to theconcentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b); and (d) characterizing thereservoir of interest based upon the comparing of (c).
 2. The method ofclaim 1, wherein the reservoir fluid within the at least one wellbore isdetermined to be connected and in a state of non-equilibrium in theevent that there are small differences between the non-equilibriumconcentration of the asphaltene component as a function of locationwithin the wellbore resulting from the simulation of (a) and theconcentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b).
 3. The method of claim 2,comprising: tuning the numerical model based on uncertainties associatedwith the analysis of (b) to obtain a tuned numerical model; repeatingthe simulation of (a) using the tuned numerical model; and repeating thecomparison of (c) using the results of the simulation of (a) using thetuned numerical model.
 4. The method of claim 1, wherein the reservoirfluid within the at least one wellbore is determined to becompartmentalized in the event that there are large differences betweenthe non-equilibrium concentration of the asphaltene component as afunction of location within the wellbore resulting from the simulationof (a) and the concentration of the asphaltene component as a functionof location within the wellbore as measured in (b).
 5. The method ofclaim 4, comprising validating the determination of compartmentalizationbased on a geochemical analysis of the hydrocarbon reservoir ofinterest.
 6. The method of claim 4, comprising performing downhole fluidanalysis (DFA) of the hydrocarbon reservoir of interest to determine acompartmentalization location of the hydrocarbon reservoir of interest.7. The method of claim 1, wherein the analyzing of (b) involves downholefluid analysis performed within the wellbore on live hydrocarbon fluidsextracted from the reservoir of interest.
 8. The method of claim 1,wherein the analyzing of (b) involves laboratory fluid analysisperformed on at least one hydrocarbon fluid sample collected from thereservoir of interest.
 9. The method of claim 1, wherein the numericalmodel is based on at least one of a depth, a length, a permeability, aporosity, or a viscosity, or any combination thereof, of the reservoirof interest.
 10. The method of claim 1, wherein a boundary condition ofthe numerical model is a zero flux boundary condition if the reservoirof interest does not have a gas charge and the boundary condition of thenumerical model is a gas influx boundary condition if the reservoir ofinterest has a gas charge.
 11. The method of claim 1, wherein thenumerical model is based on an equation of the form${\frac{\partial\left( {x_{1}/V} \right)}{\partial t} + {\nabla{\cdot \left( {\frac{x_{1}u}{V} - {\frac{M_{2}}{M\; V}{D_{12}\left( {{\gamma^{F\; H}{\nabla x_{1}}} + {\frac{\Delta\;\rho\; x_{1}V_{1}}{\rho\; R\; T}{\nabla\; P}}} \right)}}} \right)}}} = 0$where x₁ is the mole fraction of a first component of the reservoirfluid, V is the molar volume of the reservoir fluid, t is the time, ∇ isthe gradient operator, u is the pore velocity vector, M₂ is themolecular weight of a second component of the reservoir fluid, M is themolecular weight of the reservoir fluid, D₁₂ is the molecular diffusioncoefficient of the first and second components, γ^(FH) is thenon-ideality term, ρ is the mass density of the reservoir fluid, Δρ isthe difference between the mass density of the first component and themass density of the reservoir fluid, V₁ is the molar volume of the firstcomponent, R is the universal gas constant, T is the temperature of thereservoir fluid, and P is the pressure of the reservoir fluid.
 12. Themethod of claim 11, wherein the numerical model is developed bycombining the equation with a second equation representing massconservation of the reservoir fluid.
 13. The method of claim 1, whereinthe simulation of (a) comprises a three-dimensional simulation.
 14. Asystem for characterizing a hydrocarbon reservoir of interest traversedby at least one wellbore, comprising: a downhole tool configured tocollect formation fluid from the hydrocarbon reservoir of interestwithin a sample chamber disposed in a downhole tool; and a controllercomprising machine readable instructions disposed on a memory device,wherein the instructions monitor or control operations of the downholetool to: (a) use a numerical model to simulate over geological time anon-equilibrium concentration of an asphaltene component as a functionof location within the wellbore; (b) analyze fluid samples acquired fromat least one wellbore that traverses the reservoir of interest tomeasure concentration of the asphaltene component as a function oflocation within the wellbore; (c) compare the non-equilibriumconcentration of the asphaltene component as a function of locationwithin the wellbore resulting from the simulation of (a) to theconcentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b); and (d) characterize thereservoir of interest based upon the comparison of (c).
 15. The systemof claim 14, wherein the instructions monitor or control operations ofthe downhole tool to determine the reservoir fluid within the at leastone wellbore to be connected and in a state of non-equilibrium in theevent that there are small differences between the non-equilibriumconcentration of the asphaltene component as a function of locationwithin the wellbore resulting from the simulation of (a) and theconcentration of the asphaltene component as a function of locationwithin the wellbore as measured in (b).
 16. The system of claim 14,wherein the instructions monitor or control operations of the downholetool to determine the reservoir fluid within the at least one wellboreto be compartmentalized in the event that there are large differencesbetween the non-equilibrium concentration of the asphaltene component asa function of location within the wellbore resulting from the simulationof (a) and the concentration of the asphaltene component as a functionof location within the wellbore as measured in (b).
 17. The system ofclaim 14, wherein the numerical model is based on an equation of theform${\frac{\partial\left( {x_{1}/V} \right)}{\partial t} + {\nabla{\cdot \left( {\frac{x_{1}u}{V} - {\frac{M_{2}}{M\; V}{D_{12}\left( {{\gamma^{F\; H}{\nabla x_{1}}} + {\frac{\Delta\;\rho\; x_{1}V_{1}}{\rho\; R\; T}{\nabla\; P}}} \right)}}} \right)}}} = 0$where x₁ is the mole fraction of a first component of the reservoirfluid, V is the molar volume of the reservoir fluid, t is the time, ∇ isthe gradient operator, u is the pore velocity vector, M₂ is themolecular weight of a second component of the reservoir fluid, M is themolecular weight of the reservoir fluid, D₁₂ is the molecular diffusioncoefficient of the first and second components, γ^(FH) is thenon-ideality term ρ is the mass density of the reservoir fluid, Δρ isthe difference between the mass density of the first component and themass density of the reservoir fluid, V₁ is the molar volume of the firstcomponent, R is the universal gas constant, T is the temperature of thereservoir fluid, and P is the pressure of the reservoir fluid.